Extracting pairs of entities and their relations in the form of (head, relation, tail) or \((h, r, t)\)  from unstructured text is an important task in natural language processing and knowledge graph construction.
Traditional pipeline approaches [1]}, [2]}, [3]} treat entity recognition and relation classification as two separate sub-tasks.
Although flexible, pipeline methods ignore the interactions between the two sub-tasks and are susceptible for the problem of error propagation [4]}.
Therefore, recent studies focus on building joint models to obtain entities together with their relations through a unified architecture.
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